Data Rule Suggestion Engine for ERP Validation
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Solution Overview
Problem
Current enterprise resource planning (ERP) systems require significant manual effort for validation and cleansing of invalid data, as they struggle to automatically identify and correct incomplete, inaccurate, or invalid data entries across various fields.
Innovation Solution
A system that uses content type profiling to automatically suggest and generate rules for data validation and cleansing by correlating data content types with relevant rules from a knowledge base, reducing manual intervention through a rule suggestion engine and user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual validation and cleansing rules are used, then data quality can be improved, but significant manual effort and time are required
Solution Approach 1:
The system performs self-service by automatically profiling data to identify content types and generating validation rules without human intervention. The rule suggestion engine autonomously analyzes data patterns, infers appropriate validation rules, and presents them for acceptance, eliminating the need for manual rule creation while maintaining high data quality standards.
Solution Approach 2:
The system performs preliminary action by proactively profiling data and suggesting validation rules before data quality issues manifest. The content type profiler continuously monitors data and pre-generates appropriate validation rules, allowing organizations to prevent rather than react to data quality problems, thereby reducing both manual effort and time investment.
2Productivity
If automated rule generation is implemented, then manual effort is reduced, but system complexity increases
Solution Approach 1:
The content type profiler serves as an intermediary between raw data and validation rules. It abstracts the complex task of rule generation by first profiling data to identify content types, then mapping these types to appropriate validation rules. This intermediary layer simplifies the overall system architecture by breaking down the complex rule generation process into manageable, automated steps.
Solution Approach 2:
The system segments the rule generation process into distinct modular components: data profiling, content type identification, rule suggestion, and rule acceptance. Each component handles a specific aspect of the validation process independently, reducing overall system complexity while maintaining high automation efficiency. Users can interact with and validate individual rule suggestions without overwhelming complexity.
Data Source
AI summary
A method and system are presented of automatically suggesting rules for data stored in a table, with the table comprising a plurality of columns. The table is profiled to identify a content type for each of one or more of the plurality of columns. A rule knowledge base is accessed to locate rules specified for identified content types. Then, one or more of the located rules specified for identified content types are presented as suggestions. Acceptance of one or more of the suggested rules is received from a user, and the received validations are stored in the rule knowledge base. The accepted rules are applied to data for quality detection and monitoring. Embodiments are also described where columns are suggested based on a given rule.